Identification of lung regions in chest radiographs is an important pre-processing step for most types of computer analysis of digital chest radiographs, such as interstitial disease, pneumothorax, cardiomegaly and pulmonary nodules. A considerable amount of work in recent literature has addressed this topic, and various image processing methods have been applied. These methods can be basically classified into two categories. One is feature-based pixel classification and the other is ruled-based reasoning. In pixel classification systems, each pixel in the image is represented by a set of features, such as density, histogram, entropy, and gradients etc, and is classified into a region type based on the output of Neural Networks or Markov Random Field Modeling. Prior work in this subject area includes the work of McNitt-Gray et al. Feature Selection classification problem of digital chest radiograph segmentation, IEEE Trans. Med. Imaging, 1995, 14, pp 537–547, who developed a pattern classification scheme consisting of stepwise discriminate analysis as a basis for feature selection which has then used to train and test classifiers, Tsuji et al., Automated Segmentation of anatomical region in chest radiographs using an adaptive-sized hybrid neural network, Med. Phys., 25 (6), June 1998, pp 998–1007, who used an adaptive-sized hybrid neural network to classify each pixel into two anatomic classes (lung and others) according to relative pixel address, density and histogram equalized entropy and Hassegawa et al., A Shift-Invariant Neural Network for the Lung Field Segmentation in Chest Radiography, Journal of VLSI Signal Processing, No. 18, 1998, pp 241–250, who employed a shift-invariant neural network to extract the lung regions. Vittitoe et al., Identification of lung regions in chest radiographs using Markov random field modeling, Med. Phys. 25, (6), 1998, pp 976–985, developed a pixel classifier for the identification of lung regions using Markov Random Field modeling. Lung segmentation by rule-based reasoning consists of a series of steps, each containing specific processing and, usually, certain adjustable parameters. For example, Armato et al., Automated Registration of ventilation/perfusion images with digital chest radiographs., Acad. Radiology, 1997, 4, 183–192, used a combination of a global and local gray-level thresholding to extract lung regions and then smoothed the lung contours by a rolling ball technique. Duryea et al., A fully automated algorithm for the segmentation of lung fields in digital chest radiographic images, Med. Phys., 1995, 22, 99 183–191, proposed a heuristic edge tracing approach with validation against hand-drawn lung contours. Pietka, Lung Segmentation in Digital Radiographs, Journal of Digital Imaging, vol. 7, No. 2, 1994, pp 79–84, delineated lung borders using a single threshold determined from the gray-level histogram of a selected region, then refined the lung edges by gradient analysis. Xu et al., Image Feature Analysis For Computer-Aid Diagnosis: Detection of Right and Left hemi diaphragm edges and Delineation of lung field in chest radiographs, Med. Phys., 23 (9), September 1996, pp 1613–1624, determined the lung regions by detecting top lung edges and ribcage edges, then fitting the edges into smooth curves. Carrascal et al., Automatic Calculation of total lung capacity from automatically traced boundaries in postero-anterior and lateral digital chest radiographs, Med. Phys., 25 (7), July 1998, pp 1118–1131, detected the lung boundary segments in a set of automatic defined Regions of Interests (ROIs), then corrected and completed the boundary by means of interpolation and arc fitting.
Generally speaking, the methods described in the prior art are low-level processing, which operate directly on the raw image data; even through a few of them utilize embedded domain knowledge as heuristics within segmentation algorithms. These approaches pose problems when processing images of abnormal anatomy, or images with excessive noise and poor quality, because the abnormal anatomic structures or noise often confuse the segmentation routines. Thus, there exists a need for high-level analysis, incorporating both the anatomical knowledge and low-level image processing, in order to improve the performance of segmentation algorithms. To solve the problem, Brown et al., Knowledge-based method for segmentation and analysis of lung boundaries in chest x-ray images, Computerized medical Imaging and Graphics, 1998, 22, pp 463–477, presented a knowledge-based system which matches image edges to an anatomical model of the lung boundary using parametric features and reasoning mechanisms. Ginneken et al., Computer-Aided Diagnosis in Chest Radiography PhD thesis, Utrecht University, March 2001, used a hybrid system that combines the strength of a rule-based approach and pixel classification to detect lung regions. Although the latter methods demonstrate improved performance, to automatically and accurately detect lung regions is still a difficult problem. There are several factors that contribute to this difficulty including (1) a high degree of variation in chest image composition from person to person, (2) the variability in the habitus and level of inspiration of the lungs during the examination, and (3) the superimposed structures in the lung regions of chest radiographs, such as lung vasculature, ribs, and clavicles. The latter structures cause the lung boundaries to appear ambiguous, which greatly reduces the performance of low-level image processing.
To reliably segment lung regions, both low-level processing and high-level analysis must be employed, and low-level processing techniques should be constrained and directed by knowledge of the relevant local anatomy, which is supplied through high-level analysis. The present invention provides a solution to the problems of the prior art and employs a robust means to automatically segment lung regions in digital chest radiographs by using a knowledge-based model, which not only encapsulates the properties of anatomic structures, but also specifies an efficient way to detect them and evaluate their relationships.